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   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-10-27T08:39:34.890700Z",
     "start_time": "2025-10-27T08:39:33.613996Z"
    }
   },
   "source": [
    "import sqlalchemy\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "from pandas.core.dtypes.common import pandas_dtype\n",
    "from sqlalchemy import create_engine\n",
    "import pymysql\n",
    "print(sqlalchemy.__version__)\n",
    "print(pymysql.__version__)\n",
    "df=pd.DataFrame({\"班级\":[\"一年级\",\"二年级\",\"三年级\",\"四年级\"],\n",
    "                 \"男生人数\":[25,23,27,30],\n",
    "                 \"女生人数\":[19,17,20,20]\n",
    "                 })\n",
    "df"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.43\n",
      "1.4.6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "    班级  男生人数  女生人数\n",
       "0  一年级    25    19\n",
       "1  二年级    23    17\n",
       "2  三年级    27    20\n",
       "3  四年级    30    20"
      ],
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       "      <th>女生人数</th>\n",
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       "      <th>1</th>\n",
       "      <td>二年级</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>三年级</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>四年级</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ]
     },
     "execution_count": 1,
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   ],
   "execution_count": 1
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  {
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    "ExecuteTime": {
     "end_time": "2025-10-27T08:51:00.640993Z",
     "start_time": "2025-10-27T08:51:00.561009Z"
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   "cell_type": "code",
   "source": [
    "engine = create_engine('mysql+pymysql://root:123456@localhost:3306/school?charset=utf8')\n",
    "df.to_sql('students', con=engine, if_exists='replace', index=False)"
   ],
   "id": "97d04448c3ce92ff",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "execution_count": 5
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     "end_time": "2025-10-27T09:02:38.461241Z",
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    }
   },
   "cell_type": "code",
   "source": "pd.read_sql('students', con=engine)",
   "id": "f0e430166d635d9c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    班级  男生人数  女生人数\n",
       "0  一年级    25    19\n",
       "1  二年级    23    17\n",
       "2  三年级    27    20\n",
       "3  四年级    30    20"
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   "execution_count": 6
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   "cell_type": "code",
   "source": "pd.read_sql('student', con=engine)",
   "id": "571feb462539d921",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   id name gender  age address       qq               email\n",
       "0   1   张三      男   15      陕西    12345  zhangsan@itcast.cn\n",
       "1   2   李四      女   15      北京    88888        ls@itcast.cn\n",
       "2   4    1      男    1      陕西  1212131     1212131@123.com\n",
       "3   5    2      男    2      陕西     2222        2222@132.com\n",
       "4   9   王五      男   19      河南    75695         7458@qq.com"
      ],
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       "      <td>2</td>\n",
       "      <td>李四</td>\n",
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       "      <td>15</td>\n",
       "      <td>北京</td>\n",
       "      <td>88888</td>\n",
       "      <td>ls@itcast.cn</td>\n",
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       "      <th>2</th>\n",
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       "      <td>7458@qq.com</td>\n",
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     "execution_count": 8,
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   ],
   "execution_count": 8
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     "start_time": "2025-10-27T09:12:52.762584Z"
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   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df_obj=pd.DataFrame(np.arange(9).reshape((3,3)),index=[4,3,5])\n",
    "df_obj"
   ],
   "id": "1f7d014187f7ccd9",
   "outputs": [
    {
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      "text/plain": [
       "   0  1  2\n",
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       "3  3  4  5\n",
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   "cell_type": "code",
   "source": "df_obj.sort_index()",
   "id": "2d357eef63d4f9c6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   0  1  2\n",
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       "4  0  1  2\n",
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     "execution_count": 10,
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   "cell_type": "code",
   "source": "df_obj.sort_index(ascending=False)",
   "id": "1216ea2be4452c3f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   0  1  2\n",
       "5  6  7  8\n",
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   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = np.arange(12).reshape((3,4))\n",
    "df3 = pd.DataFrame(data,index=['d','b','c'],columns=['dd','aa','cc','bb'])\n",
    "df3"
   ],
   "id": "aea96dcb14b49359",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   dd  aa  cc  bb\n",
       "d   0   1   2   3\n",
       "b   4   5   6   7\n",
       "c   8   9  10  11"
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     "execution_count": 7,
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   "execution_count": 7
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  {
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     "end_time": "2025-10-27T09:32:25.329897Z",
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   },
   "cell_type": "code",
   "source": "df3.sort_index(axis=0)  #行排序",
   "id": "cfe21fea495f82ef",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   dd  aa  cc  bb\n",
       "b   4   5   6   7\n",
       "c   8   9  10  11\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "execution_count": 9
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  {
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     "end_time": "2025-10-27T09:32:32.756194Z",
     "start_time": "2025-10-27T09:32:32.731362Z"
    }
   },
   "cell_type": "code",
   "source": "df3.sort_index(axis=1)  #列排序",
   "id": "f2bbc57993322c47",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   aa  bb  cc  dd\n",
       "d   1   3   2   0\n",
       "b   5   7   6   4\n",
       "c   9  11  10   8"
      ],
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       "      <td>8</td>\n",
       "    </tr>\n",
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     },
     "execution_count": 10,
     "metadata": {},
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   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-27T09:32:49.835375Z",
     "start_time": "2025-10-27T09:32:49.815657Z"
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   },
   "cell_type": "code",
   "source": "df3.sort_values(by='cc')  #按’cc’列的数据进行排序",
   "id": "124c1304344871fb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   dd  aa  cc  bb\n",
       "d   0   1   2   3\n",
       "b   4   5   6   7\n",
       "c   8   9  10  11"
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     "execution_count": 11,
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